Potential of Artificial Intelligence in Addressing Critical Challenges Related to Water Resources

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1478

Special Issue Editors


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Rennes Institute of Chemical Sciences, University of Rennes, CEDEX 7, 35708 Rennes, France
Interests: environmental engineering; combined processes; biological treatment; advanced (electrochemical) oxidation processes
Special Issues, Collections and Topics in MDPI journals
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: advanced process control; process fault detection and diagnosis; neural networks and neuro-fuzzy systems; multivariate statistical process control; optimal control of batch processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our objective with this Special Issue is to emphasize the transformative potential of AI in addressing critical challenges related to environmental preservation, especially water resources.

This Special Issue aims to disseminate and discuss advances in AI technologies, promoting sophisticated solutions to enhance and ensure the quality of water treatment. We invite original and unpublished contributions in various application areas, including the following:

  • Artificial intelligence for water quality surveillance and management.
  • AI applications for the early detection of water pollution.
  • Optimization of water treatment processes through artificial intelligence.
  • Intelligent systems for sustainable water resource management.

We welcome contributions of unpublished research exploring various aspects of artificial intelligence for water treatment, such as the following:

  • Modeling water treatment processes, including adsorption, photodegradation, oxidation, etc.
  • Intelligent technologies for the real-time monitoring of water parameters.
  • AI-based predictive modeling for water quality.
  • Deep learning applications in the detection and elimination of contaminants.
  • AI systems for optimizing water treatment processes.
  • AI approaches for intelligent management of water networks.

This Special Issue extends an invitation to researchers, data scientists, environmental experts, and policymakers to contribute their original research, reviews, case studies, and insightful perspectives showcasing the novel applications and potential of AI and computation in the field of environmental management. We encourage a diversity of contributions to provide a comprehensive and in-depth view of these crucial advancements.

Prof. Dr. Abdeltif Amrane
Dr. Jie Zhang
Guest Editors

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Keywords

  • water treatment
  • water management
  • contaminants
  • artificial intelligence
  • deep learning applications
  • predictive modeling

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Published Papers (1 paper)

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Research

33 pages, 3222 KiB  
Article
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution
by Selma Toumi, Sabrina Lekmine, Nabil Touzout, Hamza Moussa, Noureddine Elboughdiri, Reguia Boudraa, Ouided Benslama, Mohammed Kebir, Subhan Danish, Jie Zhang, Abdeltif Amrane and Hichem Tahraoui
Water 2024, 16(23), 3380; https://doi.org/10.3390/w16233380 - 24 Nov 2024
Viewed by 457
Abstract
This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water [...] Read more.
This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications. Full article
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